ABSTRACT The capacity for self-control is one of the strongest predictors of psychological and physical well-being, with researchers estimating that 80-90% of self-control in everyday life relies on inhibitory processes. Poor inhibitory control negatively impacts health in numerous ways, including by increasing engagement in risky and impulsive behaviors that are associated with premature death and disease (e.g., reckless driving, unsafe sexual practices, aggression, binge eating). Notably, inhibitory control failure is a transdiagnostic feature of a diverse array of mental disorders (e.g., posttraumatic stress, substance use, impulse control disorders) and other harmful behaviors (e.g., suicide, violence). While it is known that inhibitory failures occur reliably more often in specific cognitive, motivational, and emotional contexts, particularly in mental illness, how such contexts impact the functional brain networks supporting inhibitory control remains largely unknown. The long- term goal of this research is to harness knowledge about the brain mechanisms of inhibition to advance etiological models of mental disorders. The objective of this proposal is to determine how functional brain networks supporting inhibitory control respond to situational challenges and to evaluate the relevance of these network adaptations for predicting self-control, psychopathology symptoms, and risky behavior. The specific aims of the proposal are to: 1) determine how functional networks adapt to contextual challenges (cognitive resource depletion, competing reward cues, negative mood induction) during inhibitory control tasks, 2) establish the validity and replicability of shifts in the functional connectome in response to contextual challenges, 3) evaluate whether context-related shifts in functional network organization predict self-control, vulnerabilities for psychopathology, and risky behavior in healthy controls, and 4) examine functional connectome metrics of inhibitory control in clinical samples. The study design involves new data collection on two cohorts: a sample of healthy controls (N = 100) and a clinical sample of individuals with a history of mental health treatment (N = 50). The healthy control sample will be assessed at two time points approximately three months apart. All participants will undergo MRI scanning and a thorough clinical/ behavioral assessment to assess self-regulation, vulnerabilities for psychopathology, psychiatric symptoms, and risky behaviors. The knowledge gained from the proposed research has the potential to significantly advance current models of inhibition by delineating how the neural networks supporting self-control flexibly adapt to situational challenges and confer risk for psychopathology and risky behaviors.